Comparison of tasselled cap transformations based ...

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Comparison of tasselled cap transformations based on the selective bands of Landsat 8 OLI TOA reflectance images a

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Qingsheng Liu , Gaohuan Liu , Chong Huang & Chuanjie Xie

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State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China Published online: 15 Jan 2015.

Click for updates To cite this article: Qingsheng Liu, Gaohuan Liu, Chong Huang & Chuanjie Xie (2015) Comparison of tasselled cap transformations based on the selective bands of Landsat 8 OLI TOA reflectance images, International Journal of Remote Sensing, 36:2, 417-441, DOI: 10.1080/01431161.2014.995274 To link to this article: http://dx.doi.org/10.1080/01431161.2014.995274

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International Journal of Remote Sensing, 2015 Vol. 36, No. 2, 417–441, http://dx.doi.org/10.1080/01431161.2014.995274

Comparison of tasselled cap transformations based on the selective bands of Landsat 8 OLI TOA reflectance images Qingsheng Liu, Gaohuan Liu*, Chong Huang, and Chuanjie Xie State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China (Received 26 January 2014; accepted 4 November 2014) Parameters for the tasselled cap transformation (TCT) have been derived for many sensors since 1976. There have been concerns about the comparability of TCT brightness (TCTB), greenness (TCTG), and wetness (TCTW) from different sensors because the number and bandwidth of spectral bands of the different sensors are not exactly the same and the derivation methods vary. In this research, comparisons between the TCT components derived from different combination images with a different number of spectral bands are considered. First, a new TCT based on a new data set from Landsat 8 Operational Land Imager top of atmosphere (OLI TOA) reflectance was developed. Then, a case study of the Yellow River Delta, China, demonstrated that TCT parameters derived from a Landsat 8 OLI TOA reference image from May 2013 were probably applicable to clear and nearly cloud-free images for spring, summer, and autumn over the Yellow River Delta. Finally, we compared the TCT components derived from selected bands of Landsat 8 OLI TOA reflectance images and those derived from images from other well-known moderateresolution worldwide remote-sensing data by statistical characteristics, correlation coefficients, the optimum index factor (OIF), and classification with a support vector machine. The result supports our conclusion (a) that two new shortwave bands (Bands 1 and 9) of the Landsat 8 OLI have little effect on derivations of the TCT components and their ability to classify land cover type and (b) that Bands 4–7 in Landsat multispectral scanner, Bands 1–4 in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Bands 1–4 in Systeme Probatoire d’Observation dela Tarre 5 are sufficient for deriving TCTB, TCTG, and TCTW components and mapping land cover.

1. Introduction Land-use and land-cover information have been identified as crucial data components in many aspects of regional planning, global change research, and environmental monitoring applications. Over the last four decades or so, multispectral images from remote-sensing sensors have increasingly become the prime source of land-use and land-cover information. Many bands of these sensors are often in the visible, near-infrared, and shortwave infrared spectral ranges, which contain information highly correlated to surface features, so analysing imagery with a natural or false colour compositing from these bands may not optimally discriminate surface features (Horne 2003). To address this problem, various techniques have been developed to enhance surface features. One of the major approaches is classifiers that have been used by remote-sensing research, which include the Iterative Self-Organizing Data Analysis Technique (ISODATA) and K-Means unsupervised classifiers, maximum likelihood, minimum distance, Mahalanobis distance, parallelepiped, spectral angle mapper, neural network, support vector machine (SVM), decision tree classifiers, and the *Corresponding author. Email: [email protected] © 2015 Taylor & Francis


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ensemble classifier. Most of these have been integrated into remote-sensing image processing software, such as PCI, ERDAS, and ENVI. Another major approach is transformations that highlight certain surface types, such as principal component analysis (PCA) and tasselled cap transformation (TCT). TCT has been widely accepted in the remote-sensing community. This is partly due to its compression of multispectral data into a few bands, for which there are clear operational savings in storage and processing (Healey et al. 2005). Compared with PCA, features derived from TCT can be directly associated with important physical parameters of the land surface that are more easily understood (Jin and Sader 2005). It has been proved that the method integrating SVM with TCT is very effective and has high accuracy (Liu and Liu 2010). However, changing to a new sensor or a new application (with a different set of relevant scene classes) requires recalculation of the TCT (Crist and Kauth 1986). Thus, TCT parameters, shown in Table 1, have been derived based on the Landsat Multispectral Scanner (MSS; Kauth and Thomas 1976), Landsat 4 Thematic Mapper (TM; Crist and Cicone 1984; Crist 1985), Landsat 5 TM (Crist, Laurin, and Cicone 1986), Landsat 7 Enhanced Thematic Mapper Plus (ETM+; Huang et al. 2002), Japan Earth Resource Satellite 1 (JERS-1) Optical Senor (OPS; Malila and Meyers 1995), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (Wang and Sun 2005), Moderate Resolution Imaging Spectroradiometer (MODIS; Lobser and Cohen 2007), Systeme Probatoire d’Observation de la Terre 5 (SPOT-5; Ivits et al. 2008), IKONOS (Horne 2003), QuickBird 2 (Yarbrough, Easson, and Kuszmaul 2005), China-Brazil Earth Resource Satellite 2 (CBERS-02; Liu and Liu 2009; Sheng, Huang, and Tang 2011), HuanJing satellite 1 (HJ-1; Liu et al. 2010; Chen, Tang, and Bian 2012) and Landsat 8 Operational Land Imager (OLI; Baig et al. 2014). Some of these have been integrated into commercial remote-sensing processing software, such as PCI, ERDAS, and ENVI. Because the shape of the tasselled-cap-like space is dependent on the spectral response of the sensors and is a spectral enhancement method, the dominant land cover of a scene, the number of scenes, the number of random samples, the number of bands, and their bandwidth used for derivation of TCT parameters all affect the TCT parameters, the components, and the shape of the tasselled-cap-like space (Yarbrough, Easson, and Kuszmaul 2012). However, the above TCT parameters of these sensors are calculated based on all the visible, near-infrared, and shortwave infrared bands acquired by these sensors, yet the number of bands and the bandwidths of these sensors are not exactly the same. Therefore, two problems that can be encountered using the conventional TCT method are that a calculation redundancy might be produced because of using highly correlated bands and that the TCT components such as brightness (TCTB), greenness (TCTG), and wetness (TCTW) calculated from the different sensors can be difficult to compare with one another because of using the different numbers and band widths of the spectral bands. So the question is whether we can use the common or frequently used band sets of these sensors to do TCT for minimizing both of these problems. In other words, is there a minimum band set for derivation of TCT? Here, it is called the ‘selective’ TCT, which means that TCT is done using band subsets such as the bands often acquired by the moderate-resolution land-remote-sensing sensors in the world. Compared with the other moderate-resolution land-remote-sensing sensors in the world, Landsat sensors have been used for more than four decades, and the Landsat 8 OLI sensor, launched recently, includes more spectral bands, and so is suitable for analysing this problem. The goal of this study is to compare TCTB, TCTG, and TCTW components derived from the selected bands of Landsat 8 OLI top-of-atmosphere (TOA) reflectance images using statistical characteristics, correlation coefficients, optimum index factor (OIF), and the discrimination capability for classifying land-use and land-cover types using the SVM based on TCTB, TCTG, and TCTW components.

At-satellite reflectance data


DN

0.1511

W

0.1973

−0.243

−0.2941

0.4743 −0.5436 0.3279 0.5524 −0.4934 0.3102

0.5860 0.6000 0.6000

Band 3

G

0.2793 −0.2435 0.1973 0.4158 −0.2819 0.2021

0.6320 −0.5620 0.5220

Band 2

0.4806 −0.5508 0.3322 0.3904 −0.4556 0.0926 0.2786

0.2909 −0.2728 0.1446 0.3561 −0.3344 0.2626

0.3037 −0.2848 0.1509 0.2043 −0.1063 0.0315

0.4330 −0.2900 −0.8290

Band 1

0.2493 −0.2174 0.1761 0.3972 −0.3544 0.2141 0.3029

B G W B G W B

B G W B G W

DN

Reflectance factor

B G Y

Comp

Digital number (DN)

Data

The tasselled cap transformation parameters of parts of sensors.

Landsat MSS B4: 0.5–0.6 B5: 0.6–0.7 B6: 0.7–0.8 B7: 0.8–1.1 Landsat 4 TM B1: 0.45–0.52 B2: 0.52–0.60 B3: 0.63–0.69 B4: 0.76–0.90 B5: 1.55–1.75 B7: 2.08–2.35 Landsat 5 TM Spectral bands is same as Landsat 4 Landsat 7 ETM+ Spectral bands is same as Landsat 4 Landsat 8 OLI B2: 0.45–0.52 B3: 0.53–0.60 B4: 0.63–0.68 B5: 0.85–0.89 B6: 1.56–1.66 B7: 2.10–2.30

Sensor

Table 1.

0.3283

−0.5424

0.5568 0.7220 0.3396 0.6966 0.6966 0.0656 0.4733

0.5585 0.7243 0.3406 0.5741 0.7940 0.1594

0.2640 0.4910 0.4910

Band 4

0.4438 0.0733 −0.6210 0.2286 −0.0242 −0.7629 0.5599 0.508 0.7276 0.0713 0.3407 −0.7117

0.5082 0.0840 −0.7112 0.3124 −0.0002 −0.6806

Band 5

−0.4559

−0.1608

0.1706 −0.1648 0.4186 0.1596 −0.2630 −0.5388 0.1872

0.1863 −0.1800 −0.4572 0.2303 −0.1446 −0.6109

Band 7


(Continued )

Baig et al. (2014)

Crist, Laurin, and Cicone (1986) Huang et al. (2002)

Crist (1985)

Crist and Cicone (1984)

Kauth and Thomas (1976)

Reference

International Journal of Remote Sensing 419

Data

−0.1031

−0.0318 0.4571

G W

B G T

Data after subtracting average value of each band

0.326 −0.311 −0.612

0.492 −0.196 0.397

0.1147

W

B G W

0.5129

−0.4064

G

0.509 −0.356 −0.312

0.610 −0.389 0.260

0.2489

0.5945

0.4395

B

0.4262

0.5224

Band 2

0.3909

Band 1

B

Comp


MODIS Nadir BRDFAdjusted reflectance data

DN

(Continued ).

ASTER B1: 0.52–0.60 B2: 0.63–0.69 B3: 0.78–0.86 B4: 1.6–1.7 B5: 2.145–2.185 B6: 2.158–2.225 B7: 2.235–2.285 B8: 2.295–2.365 B9: 2.360–2.430 MODIS B1: 0.62–0.67 B2: 0.841–0.876 B3: 0.459–0.479 B4: 0.545–0.565 B5: 1.230–1.250 B6: 1.628–1.652 B7: 2.105–2.155 SPOT5 B1: 0.50–0.59 B2: 0.61–0.68 B3: 0.78–0.89 B4: 1.58–1.75 IKONOS B1: 0.45–0.53 B2: 0.52–0.61 B3: 0.64–0.72 B4: 0.77–0.88

Sensor

Table 1.

0.560 −0.325 0.722

0.416 0.896 0.118

0.2408

−0.2744

0.567 0.819 −0.081

0.462 −0.084 −0.872

0.3132

−0.2893

0.3918

0.2808

−0.1568

0.2640

0.2512

0.3233

Band 4

0.9422

0.1184

Band 3

0.3506 0.2136 0.4882 −0.0036 −0.3122 −0.6416

−0.2417 −0.3269

−0.0737 −0.0690

0.3050 0.3571

Band 5

−0.5087

−0.4169

0.2678

0.3347 0.3169 0.1510 −0.0957 −0.1195 −0.0625 −0.4077 −0.3731 −0.1877

Band 7


Horne (2003)

Ivits et al. (2008)

Lobser and Cohen (2007)

Wang and Sun (2005)

Reference

420 Q. Liu et al.

B G T B G T B G

DN



0.2699 −0.3146

0.4127 −0.2586 0.8690

0.319 −0.121 0.652

0.3721 −0.3857

0.4771 −0.3462 −0.2530

0.542 −0.331 0.375

0.4855 −0.5530

0.4676 −0.4520 −0.4198

0.490 −0.517 −0.639

0.7436 0.6682

0.6182 0.7804 −0.0671

0.604 0.780 −0.163

Notes: Comp, component; B, brightness; G, greenness; W, wetness; Y, yellowness; T, third component. The wavelength unit of Band is μm.

Quickbird 2 B1: 0.45–0.52 B2: 0.52–0.60 B3: 0.63–0.69 B4: 0.76–0.90 CBERS-02 CCD B1: 0.45–0.52 B2: 0.52–0.59 B3: 0.63–0.69 B4: 0.77–0.89 HJ-1 B CCD2 B1: 0.43–0.52 B2: 0.52–0.60 B3: 0.63–0.69 B4: 0.76–0.90


Liu et al. (2010)

Liu and Liu (2009)

Yarbrough, Easson, and Kuszmaul (2005)


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2. Methods


Figure 1 shows the whole flow chart of this research.

2.1. Data acquisition and preprocessing Four decades of Landsat imagery provide a unique and the longest continuously acquired collection of space-based moderate resolution land-remote-sensing data for use in agriculture, geology, forestry, regional planning, global change research, and environmental monitoring. Images from the Landsat 8 satellite launched on 11 February 2013 have been available since 30 May 2013 (USGS 2013a). Compared with earlier Landsat satellites, Landsat 8 has several new characteristics of its spectral bands, spectral range, and radiometric resolution. Landsat 8 carries two instruments, the OLI and the Thermal Infrared Sensor (TIRS). The TIRS provides two longwave thermal bands that collect data at 100 m, but resample to 30 m to match OLI multispectral bands. OLI collects image data every 16 days in nine shortwave bands (USGS 2014). The pixel size of Bands 1–7 and 9 is 30 m and that of Band 8 is 15 m. OLI provides compatibility with historical Landsat data and improves measurement capabilities. Ivits et al. (2008) showed that the TCT parameters were dependent on season and geographical location, and the TCT components were not stable between different seasons and geographical locations. Therefore, 13 Landsat 8 OLI images were used in this study (Table 2). According to the research results from Yarbrough, Easson, and Kuszmaul (2012), a set of 10–12 scenes of appropriate land cover would be sufficient. Therefore a set of five pairs of clear and nearly cloud-free images representing both growing season and non-growing season conditions of a variety of landscapes of the People’s Republic of China were used to derive the TCT parameters. Another three OLI images from Path 121, Row 34 of the Worldwide Reference System for the Yellow River Delta Natural Reserve (YRDNR) in China were used for the purposes of seasonal dependence analysis, which were acquired in the summer and autumn months (Table 2). Huang et al. (2002) demonstrated the necessity to convert digital numbers to at-satellite reflectances when atmospheric correction was not feasible, and a TCT based on at-satellite reflectance is more appropriate for regional applications where atmospheric correction is not feasible. So raw digital numbers were converted to top of atmosphere (TOA) reflectances using radiometric rescaling coefficients provided in the product metadata file, as described on the Landsat website (USGS 2013b).

2.2. Extraction of tasselled cap transformation parameters The TCT method is called tasselled cap transformation because of its cap shape. TCTB is responsive to physical properties that affect overall reflectance. TCTG responds to a combination of high absorption of chlorophyll in the visible bands and high reflectance of leaf structure in the near-infrared band, which is characteristic of healthy green vegetation. TCTW is responsive to the amount of moisture retention in vegetation or soil. Although TCT parameters based on Landsat 8 at-satellite reflectances were derived by Baig et al. (2014), for comparison of the different TCT components from the different combination images with the different number of the spectral bands, we needed to derive our own TCT parameters based on Landsat 8 OLI TOA reflectance because we could not get the same random samples nor use exactly the same method as Baig et al. (2014). In this research, TOA reflectance-based TCT was derived using the method described by Jackson (1983). The shape of the tasselledcap-like space was easily defined using 2000 randomly selected pixels for each scene while greatly reducing the processing time (Yarbrough, Easson, and Kuszmaul 2012). In this


International Journal of Remote Sensing

Figure 1.

The whole flow chart of this research.

423


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Q. Liu et al.

Table 2.

Landsat 8 OLI images used in this study.

Path

Row

121

34

Yellow River Delta, east of China

116

29

127

32

143

29

123

45

Helongjiang Province, northeast of China Inner Mongolia Autonomous Region, north of China Xinjiang Uygur Autonomous Region, northwest of China Gudong Province, south of China

Geographic location

Acquisition date

Season

30 May 2013 15 June 2013 3 September 2013 5 October 2013 6 November 2013 12 April 2014 16 September 2013 27 May 2014 29 September 2013

Spring* Summer Early autumn Middle autumn* Late autumn Spring* Autumn* Spring* Autumn*

22 April 2013 28 August 2013

Spring* Autumn*

23 January 2014 3 October 2013

Spring* Autumn*

Note: The images labelled with * were a set of five pairs of clear and nearly cloud-free images used to derive the TCT parameters.

research, more than 2000 random samples of dry soil, wet soil, dense vegetation, and water were selected from each of the 10 OLI scenes according to field investigation data, experimental knowledge, and a two-dimensional scatter plot between the normalized difference ρ6 moisture index (NDMI ¼ ρ5 ρ5 þ ρ6 , where ρ5 and ρ6 are the TOA reflectances of Bands 5 and 6 of Landsat 8 OLI listed in Table 1, respectively) and the normalized difference vegetation ρ5 index (NDVI ¼ ρ4 ρ4 þ ρ5 , where ρ4 and ρ5 are the TOA reflectances of Bands 5 and 6 of Landsat 8 OLI listed in Table 1, respectively). Figure 2 shows the locations of dry soil, wet

Figure 2. Dry soils, wet soils, dense vegetation, and water sample pixels on NDVI and NDMI scatter plots from the 400 × 400 subset image of May 2013 over the Yellow River Delta Natural Reserve, China.



425

soil, dense vegetation, and water samples in the scatter plot between NDVI and NDMI of the image acquired on 30 May 2013 in the YRDNR. To derive the first dimension, TCTB, first the differences between the mean TOA reflectance values of dry (Xds ) and wet soil sample points (Xws ) were selected to calculate the soil vector components (bi ¼ ðX ds X ws Þi , i ¼ 1; 2; . . . n, i stands for the number of bands). Then, the TCTB coefficients (TCTBRCi ¼ biB ) were obtained by dividing each of the vector s ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi! n P . Finally, the TCTB b2i components (bi ) by the normalization factor B ¼ i¼1 n P TCTB ¼ TCTBRCi Xi was then calculated by the linear transformation of the i¼1

image bands (Xi ) with the TCTB coefficients (TCTBRCiw ). To derive the second dimension, TCTG, first the difference between the mean values of dense vegetation sample points (Xdv ) and the dry soil sample points (Xds ) were selected to calculate each band and subsequently a Gram– Schmidt orthogonalization was implemented to get the green vector components n P ðX dv X ds Þi TCTBRCi ) which ensured (gi ¼ ðX dv X ds Þi Dg TCTBRCi , Dg ¼ i¼1

that the green vector component (gi ) was orthogonal to the soil vector components (bi ). Then, the TCTG coefficients (TCTGRCi ¼ gi by dividing each of the vector G ) were obtained sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n ! P 2 . Finally, the TCTG gi components (gi ) by the normalization factor G ¼ i¼1 n P TCTG ¼ TCTGRCi Xi was then calculated by the linear transformation of the i¼1

image bands (Xi ) with the TCTG coefficients (TCTGRCi ). The third component, TCTW, was derived following the method described earlier using the differences among dry soil, dense green vegetation, and water body points, where the wet vector components were orthogonalized to both TCTB and TCTG. A more detailed description of the whole algorithm can be found in Jackson (1983). Thus three sets of the TCT parameters for growing season, non-growing season, and all combined images were extracted from the sample points (dry soil, wet soil, dense vegetation, and water) of the five growing season images, five non-growing images, and all 10 images of China.

2.3. OIF based on tasselled cap transformation components OIF is one of the most common statistical methods and was applied to determine the most favourable three-band combinations. OIF is based on total variance within bands and the correlation coefficient between bands (Qaid and Basavarajappa 2008). The equation used to compute the OIF for TCTB, TCTG, and TCTW is: 2

3 σðiÞ 6 7 6 i¼1 7 OIF ¼ 6 3 7; 4P 5 jrðijÞj 3 P

i; j¼1

(1)

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Q. Liu et al.


where OIF is the optimum index factor, σðiÞ is the standard deviation of i band, and rðijÞ is the correlation matrix value between i band and j band.

2.4. Classification based on tasselled cap transformation components SVM, a new method of classification, has recently attracted the attention of the remote-sensing community. SVM is a system derived from statistical learning theory, which separates classes with a decision surface that maximizes the margin between classes. The surface is often called the optimal hyper plane, and data points closest to the hyper plane are called support vectors (Hsu, Chang, and Jin 2007). The implementation of SVM includes the selection of kernel functions (linear, polynomial, radial basis function, and sigmoid) and a penalty parameter, which influence the classification results. The radial basis function is often selected as the kernel, which obtains better classification results (Zhu and Blumberg 2002). In recent years, many imagery classification methods based on SVM have been published (Huang, Davis, and Townshend 2002; Tuia and Valls 2009). However, this is insufficient to achieve the well-known classification accuracy, because the general SVM does not consider the high dimensions and redundancy of multispectral images (Ma and Wang 2008). Therefore, feature extraction and image classification algorithms based on PCA and SVM were developed. It has been proved that such algorithms could reduce computational time with the reduced dimensionality of features, while improving classification accuracy (Tan, Lani, and Lai 2007; Sahak et al. 2010). Compared with PCA components, TCTB, TCTG, and TCTW have specific physical characteristics and capture the majority of variations associated with land cover information, while reducing the dimensionality of the spectral bands. Thus, a method integrating SVM with TCT was developed. Liu and Liu (2010) indicated that this method was effective and had high classification accuracy. The SVM method integrated into ENVI 5.0 was used in the present work, and the radial basis function was selected as the kernel. As training data, we used farm land, abandoned land, tidal flats and wet soils, grassland, shrub land, forests, sea, and water body samples according to the scatter plot between TCTG and TCTW derived from the 30 May 2013 image and field investigation data and experimental knowledge on the YRDNR, giving the main land cover types over the Yiqianer Natural Reserve of the Yellow River Delta. More than 9400 farm land, 700 abandoned land, 62,000 tidal flats and wet soils, 26,000 grassland, 1600 shrub land, 600 forest, 5300 sea, and 13,200 water body samples were selected. Field investigation data (52 points for land cover) were recorded over the study area using a Trimble GeoXT 3000 Geographical Positioning System (GPS) between 14 and 22 May 2013.

3. Results and discussion 3.1. The tasselled cap transformation Because the ninth Landsat band (Band 9, 1360–1390 nm) is used to detect cirrus and has little information on land use and land cover, it is not used in the derivation of TCT parameters. Table 3 gives the coefficients for the derived TCT based on a set of five pairs of clear and nearly cloud-free Landsat 8 OLI TOA reflectance images. All seven multispectral bands had positive loadings in the brightness component, independent of season (Table 3). The near infrared band (Band 5), first shortwave infrared band (Band 6), and second shortwave infrared band (Band 7) were more important for


427

Table 3. Tasselled cap transformation coefficients for seven multispectral bands of a set of five pairs of Landsat 8 OLI TOA reflectance images.


Acquisition date Component Band 1 Growing season Brightness Greenness Wetness Non-growing Brightness season Greenness Wetness The combined Brightness Greenness Wetness

Band 2

Band 3

Band 4

Band 5

Band 6

Band 7

0.0279 0.0484 0.1014 0.2051 0.3078 −0.1714 −0.2089 −0.2341 −0.3850 0.8234 0.2700 0.2373 0.1913 0.1401 −0.4487 0.0341 0.0566 0.1274 0.2376 0.3627 −0.1639 −0.2029 −0.2312 −0.3772 0.8247 0.2633 0.2255 0.1504 0.0720 −0.5045 0.0312 0.0528 0.1153 0.2225 0.3372 −01674 −0.2056 −0.2319 −0.3802 0.8246 0.2665 0.2311 0.1700 0.1048 −0.4790

0.6418 0.0094 −0.5784 0.6448 −0.0293 −0.5887 0.6440 −0.0102 −0.5847

0.6618 0.2144 −0.5278 0.6129 −0.2351 −0.5009 0.6364 −0.2264 −0.5142

the brightness component and wetness component. The red band (Band 4) and the near infrared band (Band 5) were more important for the greenness component. The new band (Band 1, 433–533 nm) of all seven bands contributed the least to the TCTB, less to the TCTG and more to the wetness component than the blue, green, and red bands (Bands 2–4). Because the dry soil, wet soil, dense vegetation, and water samples from a set of five pairs of OLI TOA reflectance images were different, the TCT coefficients for growing season, non-growing season, and combined images were not exactly the same, which indicated that the season and geographical location of the images had impacts on the TCT coefficients. Table 4 gives the correlation coefficients between the TCTG of each scene (called TCTG), all five growing season scenes (called TCTGgs) and non-growing season scenes (TCTGngs), all 10 scenes (TCTall) and the NDVI of each scene and their OIF values of TCTB, TCTG and TCTW (called OIF, OIFgs or OIFngs and OIFall, respectively). The correlation coefficients between the TCTG and NDVI (called CC) of 6 of the 10 scenes were the largest among the correlation coefficients between the TCTG and NDVI (CC), TCTGgs and NDVI (called CCgs), TCTGngs and NDVI (called CCngs), and TCTall and NDVI (called CCall), while two CCall of the 10 scenes were the largest among the CC, CCgs or CCngs and CCall. Four of the 10 scenes had the relationship CC > CCgs or

Table 4. Path/row 116/29 121/34 123/45 127/32 143/29

TCT coefficients between TCTG and NDVI and the OIF values of 10 scenes. Date

CC

CCgs or CCngs

CCall

OIF

16 September 2013 12 April 2014 30 May 2013 5 October 2013 3 October 2013 22 January 2014 29 September 2013 27 May 2014 22 April 2013 28 August 2013

0.7613 0.8388 0.9389 0.6273 0.9800 0.9293 0.8855 0.9307 0.8529 0.9661

0.7586 0.8217 0.9348 0.6323 0.9803 0.9229 0.8766 0.9326 0.8460 0.9648

0.7549 0.8887 0.9371 0.6317 0.9780 0.9076 0.8407 0.9515 0.7792 0.9649

0.0873 0.1602 0.1817 0.1506 0.1292 0.2452 0.1805 0.1181 0.1619 0.2197

OIFgs or OIFngs OIFall 0.0874 0.1227 0.1756 0.1415 0.1293 0.2444 0.1732 0.1279 0.2054 0.2093

0.0889 0.1329 0.1718 0.1509 0.1314 0.2343 0.1660 0.1323 0.2323 0.2098


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CCngs > CCall, and one of the 10 scenes had the relationship CC < CCgs or CCngs < CCall. Four of the 10 scenes had the relationship OIF < OIFgs or OIFngs < OIFall, and five OIFall of the 10 scenes were the largest among the OIF, OIFgs or OIFngs and OIFall, and another five OIF of the 10 scenes were the largest among the OIF, OIFgs or OIFngs and OIFall. Therefore, the TCT coefficients derived from each scene were more appropriate for that particular scene. In other words, a recalculation of TCT coefficients for each new scene was necessary. In the following section, all five OLI images from Path 121, Row 34 of the Worldwide Reference System for the YRDNR in China were used for analysis on seasonal effects and comparison of TCTB, TCTG, and TCTW components derived from the selective bands of Landsat 8 OLI TOA reflectance images. 3.2. TCT component characteristics derived from the selective bands Although the ninth band (Band 9, 1360–1390 nm) had little information on land use and land cover, here it was still used to derive the TCT parameter in order to evaluate quantitatively the loadings on TCT components. Table 5 gives the derived TCT parameters for all nine bands of the Landsat 8 OLI TOA reflectance images. All spectral bands had positive loadings in the brightness component, independent of season (Table 5). Loading of the new band (Band 9, 1360–1390 nm) in TCTB, TCTG, and TCTW was very small. Also, another new band (Band 1, 433–533 nm) contributed less to the TCTB, which indicated that TCTB based on Landsat 8 OLI TOA reflectance images could be derived without these two new bands. However, depending on the season, except for the image acquired on 15 June 2013, Band 1 had a gradual loss of loadings on the TCTB with the reverse effect on the TCTW from May to November images, whereas Bands 2–4 had a gradual increase of loadings on the TCTW. Band 5 had a gradual loss of loadings on the TCTB from May to November images. Band 6 (1560–1660 nm) had the opposite effect on the greenness component. A possible reason is that the May 2013 images showed high contrast between dry and wet soils, but the vegetation was not in the high biomass period and water areas appeared as light blue tones. Images from October 2013 showed significantly less contrast between the dry and wet soils, but vegetation was dense and water areas appeared very blue or dark blue. The imagery acquired in spring and autumn was suitable to optimally discriminate the surface features of land covers in the Yellow River Delta. Therefore, both TCT coefficients derived from the images in May and October were applied to all five images. Figure 3 suggests that the TCT parameters derived from the image in May (called the spring TCT) can differentiate among dry soils, wet soils, vegetation, and water in the greenness–wetness space slightly better than the parameters derived from the image in October (called the autumn TCT). Considering the dynamic changes of landscapes represented by the five images and the discrimination ability, the TCT parameters derived from the May image are probably applicable to clear and nearly cloud-free images in spring, summer, and autumn over the YRDNR in China. The image from 30 May 2013 was most appropriate for regional land cover classification. According to the above results, the Landsat 8 OLI TOA reflectance images from 5 October 2013 and 30 May 2013 were used to compare TCTB, TCTG, and TCTW derived from the selected bands of Landsat 8 OLI TOA reflectance images, based on the TCT parameter derived from 30 May 2013 by the mean value, standard deviation,

Brightness Greenness Wetness Brightness Greenness Wetness Brightness Greenness Wetness Brightness Greenness Wetness Brightness Greenness Wetness

30 May 2013

6 November 2013

5 October 2013

3 September 2013

15 June 2013

Component 0.0814 −0.1467 0.1729 0.0148 −0.1513 0.3267 0.0798 −0.0942 0.2176 0.0568 −0.1323 0.2403 0.0022 −0.1430 0.4046

Band 1 0.1146 −0.1830 0.1230 0.0354 −0.1937 0.2798 0.1184 −0.1301 0.1680 0.0833 −0.1789 0.2023 0.0144 −0.1921 0.3619

Band 2 0.1843 −0.2145 0.0538 0.0933 −0.2514 0.2054 0.2003 −0.1546 0.1050 0.1421 −0.2564 0.1792 0.0363 −0.3102 0.3113

Band 3 0.2617 −0.4179 0.0125 0.1615 −0.4730 0.1322 0.2841 −0.3709 0.1059 0.2320 −0.5154 0.1629 0.0934 −0.5007 0.2472

Band 4 0.4022 0.8234 −0.5788 0.3899 0.7671 −0.2974 0.3662 0.8688 −0.6064 0.3256 0.7667 −0.3618 0.3152 0.6864 −0.0393

Band 5 0.5733 −0.0290 −0.5517 0.6140 −0.0411 −0.5523 0.6062 −0.0265 −0.5595 0.6136 0.0617 −0.5977 0.6309 0.1221 −0.4666

Band 6

Tasselled cap coefficients for all nine bands of Landsat 8 OLI TOA reflectance images in the Yellow River Delta.

Acquisition date

Table 5.

0.6224 −0.2132 −0.5590 0.6594 −0.2501 −0.6015 0.5977 −0.2390 −0.4705 0.6582 −0.1656 −0.5954 0.7017 −0.3311 −0.5727

Band 7


0.0007 0.0000 0.0007 0.0005 0.0004 0.0014 −0.0066 0.0026 0.0120 0.0000 0.0000 0.0012 0.0010 0.0017 0.0014

Band 9


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Figure 3. Greenness and wetness scatter plots from the 400 × 400 subset image over the Yellow River Delta Natural Reserve, China. (a), (c), (e), (g), and (i) are greenness–wetness scatter plots calculated with the spring TCT parameters, and (b), (d), (f), (h), and (j) the greenness–wetness scatter plots calculated with the autumn TCT parameters, based on 30 May, 15 June, 3 September, 5 October, and 6 November 2013 images.



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coefficient of variation, correlation coefficients, and OIF, which were divided into five groups according to the spectral bands listed in Table 1. Group 1 was similar to Bands 1–4 in IKONOS, QuickBird2, CBERS and HJ; Group 2 was similar to Bands 4–7 in Landsat MSS, Bands 1–4 in ASTER, and Bands 1–4 in SPOT5; Group 3 was similar to Bands 2–7 in Landsat TM and ETM+; Group 4 was similar to Bands 1–7 in Landsat 8, and Group 5 was similar to Bands 1–7 and 9 in Landsat 8. Table 6 gives the TCT coefficients of the five groups derived from the selected bands of the image acquired on 30 May 2013. The 2D scatter plots among the TCTB, TCTG, and TCTW from the five groups can be seen in Figures 4–6. Figures 4 and 6 demonstrate the shape of the tasselled-cap-like space. The 2D scatter plots among the TCTB, TCTG, and TCTW from Group 1 (Figures 4(b), 5(a) and 6(a)) were different from the 2D scatter plots from the other four groups, especially the 2D scatter plots between TCTB and TCTW and between TCTW and TCTG, which might be due to the lack of a shortwave infrared band (Band 6). Brightness and greenness components based on Bands 2–7 (Group 3) had the highest coefficient of variation of all five groups, and the coefficient of variation of its wetness component was a little less than those of Group 4 and Group 5, which indicates again that the two new bands of Landsat 8 OLI TOA reflectance images contribute little to TCTB, TCTG, and TCTW. The brightness components in all groups except Group 1 were perfectly correlated. The correlation of the greenness component was consistently high across all five groups. The greenness component images of the five groups were similar to the NDVI of the image from 30 May 2013. These images were perfectly correlated and revealed the spatial pattern of green vegetation, which indicates that Bands 3–5 are the basis upon which the greenness component could be effectively derived. In fact, the third TCT component of Group 1 could not be called the wetness component without the shortwave infrared band. The wetness components of all but Group 1 were perfectly correlated, and the correlation coefficients between the wetness components of those four groups and the NDMI exceeded 0.61. This indicates that the shortwave infrared bands (Bands 6 and 7) are very important for deriving the TCTB and TCTW (Tables 7 and 8).

3.3. OIF values of TCT components derived from the selected bands Table 6 lists the statistical characteristics of the TCT components calculated with the parameters in Table 5, based on the 30 May 2013 image over the YRDNR. Table 7 presents correlations between TCTB, TCTG, and TCTW from the five groups calculated with the parameters in Table 5, based on the 30 May 2013 image over the YRDNR. OIF values of the five groups were statistically calculated using TCTB, TCTG, and TCTW rendered as R-G-B (Table 9). The combination of the brightness, greenness, and wetness of whole-scene images showed slightly higher values of OIF than the subset images over the YRDNR, which indicates that the OIF value is relative to land cover; the larger the region covered and the greater the land-cover information, the larger the OIF value. At the same geographical location, OIF values calculated with TCTB, TCTG, and TCTW from the 30 May 2013 image were larger than those from the 5 October 2013 image. The OIF values from Group 3 calculated from the 30 May 2013 YRDNR image were similar to those from Groups 4 and 5 (Table 9).

Brightness Greenness Third Brightness Greenness Wetness Brightness Greenness Wetness Brightness Greenness Wetness Brightness Greenness Wetness

Group 1

Group 5

Group 4

Group 3

Group 2

Components

Band 2 0.2176 −0.2726 0.1410 – – – 0.1150 −0.1864 0.1335 0.1146 −0.1830 0.1230 0.1146 −0.1830 0.1230

Band 1

– – – – – – – – – 0.0814 −0.1467 0.1729 0.0814 −0.1467 0.1729 0.3500 −0.3513 0.0005 0.2394 −0.2879 0.0681 0.1849 −0.2191 0.0652 0.1843 −0.2145 0.0538 0.1843 −0.2145 0.0538

Band 3 0.4969 −0.6227 −0.1175 0.3399 −0.5316 0.0193 0.2626 −0.4257 0.0308 0.2617 −0.4179 0.0125 0.2617 −0.4179 0.0125

Band 4 0.7637 0.6438 −0.9830 0.5224 0.7698 −0.7265 0.4035 0.8276 −0.6077 0.4022 0.8234 −0.5788 0.4022 0.8234 −0.5788

Band 5

Band 6 – – – 0.7445 −0.2048 −0.6835 0.5752 −0.0363 −0.5508 0.5733 −0.0290 −0.5517 0.5733 −0.0290 −0.5517

TCT coefficients of the five groups derived from the selected bands of the image acquired on 30 May 2013.

Groups

Table 6.

– – – – – – 0.6245 −0.2231 −0.5517 0.6224 −0.2132 −0.5590 0.6224 −0.2132 −0.5590

Band 7


– – – – – – – – – – – – 0.0007 0.0000 0.0007

Band 9

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Figure 4. (a) The false colour combination image RGB654 of subset of YRDNR; (b), (c), (d), (e), and (f) were scatter plots between TCTB and TCTG of Group 1, Group 2, Group 3, Group 4, and Group 5, respectively.

Figure 5. (a), (b), (c), (d), and (e) were scatter plots between TCTB and TCTW of Group 1, Group 2, Group 3, Group 4, and Group 5, respectively.


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Figure 6. (a), (b), (c), (d), and (e) were scatter plots between TCTW and TCTG of Group 1, Group 2, Group 3, Group 4, and Group 5, respectively.

3.4. Classification of TCT components derived from the selected bands The Landsat 8 OLI TOA reflectance image from 30 May 2013 over the YRDNR was used to quantify the discrimination ability for classifying land-use and land-cover types using the SVM, based on the TCTB, TCTG, and TCTW. The confusion matrix (1851 random samples) indicated that the SVM classification result based on TCTB, TCTG, and TCTW derived from Bands 1–7 and 9 of Landsat 8 OLI image was superior and reduced the misclassification between baysalt fields and sea and water bodies (Table 10). However, despite the bands selected, the overall precision of TCTB, TCTG, and TCTW from the classification results based on the five groups was similar at 87.63%, 88.44%, 87.84%, 91.46%, and 92.38%. The main errors were commission and omission errors for baysalt fields, shrub land, forests, and abandoned land, especially the misclassification between baysalt fields and sea and water bodies.

4. Conclusions Because the dry soil, wet soil, dense vegetation, and water samples from a set of five pairs of OLI TOA reflectance images were different, the TCT coefficients for spring and autumn and combined images were not exactly the same, which indicates that the season and geographical location of the images has an impact on the TCT coefficients. Considering dynamic landscape changes represented by the five images and discrimination ability, TCT parameters derived from the Landsat 8 OLI TOA reference image from


435


Table 7. Statistical characteristics of the TCT components of the five groups calculated with 30 May 2013 image. Groups

Components

Minimum

Maximum

Mean

Standard deviation

Coefficient of variation

Group 1

Brightness 1 Greenness 1 Wetness 1 Brightness 2 Greenness 2 Wetness 2 Brightness 3 Greenness 3 Wetness 3 Brightness 4 Greenness 4 Wetness 4 Brightness 5 Greenness 5 Wetness 5

0.0875 −0.2803 −0.5575 0.0557 −0.1704 −0.5962 0.0617 −0.1529 −0.7462 0.0714 −0.1766 −0.7208 0.0714 −0.1766 −0.7208

0.6106 0.2670 −0.0098 0.7225 0.3404 −0.0156 0.8948 0.3814 −0.0005 0.9054 0.3642 0.0201 0.9054 0.3642 0.0201

0.2815 −0.0793 −0.1579 0.2584 −0.0209 −0.1853 0.2698 −0.0115 −0.1782 0.2815 −0.0307 −0.1532 0.2815 −0.0307 −0.1532

0.0682 0.0663 0.0778 0.1074 0.0567 0.1141 0.1272 0.0610 0.1308 0.1270 0.0624 0.1293 0.1270 0.0624 0.1293

0.2423 0.8359 0.4929 0.4155 2.7092 0.6158 0.4713 5.3019 0.7340 0.4513 2.0302 0.8442 0.4513 2.0302 0.8442

Group 2

Group 3

Group 4

Group 5

Table 8. Correlation coefficients between the TCT components of the five groups calculated with the 30 May 2013 image.

Brightness Brightness Brightness Brightness Brightness

Greenness Greenness Greenness Greenness Greenness NDVI

Wetness Wetness Wetness Wetness Wetness NDMI

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

Brightness 1

Brightness 2

Brightness 3

Brightness 4

Brightness 5

1.0000

0.9242 1.0000

0.8976 0.9942 1.0000

0.8995 0.9939 0.9999 1.0000

0.8995 0.9939 0.9999 1.0000 1.0000

Greenness 1

Greenness 2

Greenness 3

Greenness 4

Greenness 5

NDVI

1.0000

0.9866 1.0000

0.9799 0.9977 1.0000

0.9829 0.9982 0.9998 1.0000

0.9829 0.9982 0.9998 1.0000 1.0000

0.9657 0.9602 0.9649 0.9664 0.9664 1.0000

Wetness 1

Wetness 2

Wetness 3

Wetness 4

Wetness 5

NDMI

1.0000

0.9616 1.0000

0.9308 0.9942 1.0000

0.9294 0.9940 0.9999 1.0000

0.9294 0.9940 0.9999 1.0000 1.0000

0.3950 0.6106 0.6603 0.6628 0.6628 1.0000

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Table 9.

Group Group Group Group Group Group Group Group Group Group Group Group Group Group Group Group Group Group Group Group

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

OIF values and rankings of the five groups. OIF

Rank

Acquisition date

0.1211 0.1540 0.1812 0.1817 0.1817 0.1118 0.1400 0.1590 0.1589 0.1589 0.1060 0.1270 0.1496 0.1501 0.1501 0.0893 0.1034 0.1151 0.1150 0.1150

5 4 2 1 1 5 4 1 2 2 5 4 2 1 1 5 4 1 2 2

30 May 2013 30 May 2013 30 May 2013 30 May 2013 30 May 2013 5 October 2013 5 October 2013 5 October 2013 5 October 2013 5 October 2013 30 May 2013 30 May 2013 30 May 2013 30 May 2013 30 May 2013 5 October 2013 5 October 2013 5 October 2013 5 October 2013 5 October 2013

Geographic location The whole The whole The whole The whole The whole The whole The whole The whole The whole The whole YRDNR YRDNR YRDNR YRDNR YRDNR YRDNR YRDNR YRDNR YRDNR YRDNR

scene scene scene scene scene scene scene scene scene scene

May 2013 are probably applicable to clear and nearly cloud-free images from spring, summer, and autumn over the Yellow River Delta in China. The image from 30 May 2013 was most appropriate for regional land cover classification. Bands 3–5 was the basis upon which the greenness component could be effectively derived, and the shortwave infrared band (Band 6) was very important for deriving the TCTB and TCTW. However, despite the bands selected, the overall precision of TCTB, TCTG, and TCTW from the five group-based classification results was similar. The result supports our conclusion that the two new shortwave bands (Bands 1 and 9) of the Landsat 8 OLI had little effect on derivations of the TCT components and their abilities to classify land-cover type. This result suggests that Bands 4–7 in Landsat MSS, Bands 1–4 in ASTER, and Bands 1–4 in SPOT-5 (Group 2) were sufficient for deriving TCTB, TCTG, and TCTW and classifying land cover types. Future research should include comparison of TCTB, TCTG, and TCTW derived from images of the various sensors listed in the five groups acquired on similar dates. It should also consider including more scenes, discussion of the seasonal and geographical location dependence of the TCT, how to evaluate more objectively the quality of the different TCT coefficients, and the use of time-series TCTB, TCTG, and TCTW derived from the sensors to study regional land use and land cover change.

Funding This research work was jointly supported by grants from the National Science-Technology Support Plan Projects [Project No. 2013BAD05B03]; National Natural Science Foundation of China [Project No. 41271407], [Project No. 41023010]; Strategic Priority Research Program [Project No. XDA05050601]; International Cooperation in Science and Technology Special Project [Project No. 2013DFA91700]; plus a Special Fund for Environmental Scientific Research for Public Welfare grant [Project No. 201109043].

Group 2

65 99 3

Water body

(1637/1851) 88.44%

1

(1622/1851) 87.63% 478 38 27 97 15 4

482 15 15

Group 1

Sea Water body Baysalt field Farm land Tidal flats and wet soil Grass land Shrub land Forest land Abandoned land Overall accuracy = Sea Water body Baysalt field Farm land Tidal flats and wet soil Grass land Shrub land Forest land Abandoned land Overall accuracy =

Sea

21 2 35

1

20 2 35

Baysalt field

Confusion matrix of the 1851 random samples (pixels).

Class

Table 10.

5

101 30

1

5

103

Farm land

4

5 9 4 1 569

7

230 7

7

4 1

10

195 16

1 3

30

5

Grass land

580

1 10 4

Tidal flats and wet soil

3 60 4 9

11 60 5

Shrub land

3 35

4 33

(Continued )

32

2

35

3

Forest Abandoned land land



Group 4

1

1

(1693/1851) 91.46%

1

8 3 35

53 14 35

Baysalt field

24 103 3

Water body

(1626/1851) 87.84% 525 29 22 103 7 4

470 18 19

Group 3

Sea Water body Baysalt field Farm land Tidal flats and wet soil Grass land Shrub land Forest land Abandoned land Overall accuracy = Sea Water body Baysalt field Farm land Tidal flats and wet soil Grass land Shrub land Forest land Abandoned land Overall accuracy =

Sea

(Continued ).

Class

Table 10.

103 38

3

238 2

2

57 1

4

1

9

1 46

Shrub land

234 3

13

Grass land

566

4 12 4

4

1

1

575

3 7 4

Tidal flats and wet soil

103 35

1

Farm land

5 40

12 40

26

1 1

1

20

1 2

Forest Abandoned land land


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Group 5

Sea 527 32 Water body 19 103 Baysalt field 8 4 Farm land Tidal flats and wet 2 soil Grass land Shrub land Forest land Abandoned land Overall accuracy = (1710/1851) 92.38%

6 3 35 103 33

1 578

6 7 2

3

239

1 6 1 58


4 40 2

27

1


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